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| #!/usr/bin/env python3` | |
| import glob | |
| import os | |
| import shutil | |
| import torch | |
| from tests import get_tests_data_path, get_tests_output_path, run_cli | |
| from TTS.tts.utils.languages import LanguageManager | |
| from TTS.tts.utils.speakers import SpeakerManager | |
| from TTS.utils.generic_utils import get_user_data_dir | |
| from TTS.utils.manage import ModelManager | |
| MODELS_WITH_SEP_TESTS = [ | |
| "tts_models/multilingual/multi-dataset/bark", | |
| "tts_models/en/multi-dataset/tortoise-v2", | |
| "tts_models/multilingual/multi-dataset/xtts_v1.1", | |
| "tts_models/multilingual/multi-dataset/xtts_v2", | |
| ] | |
| def run_models(offset=0, step=1): | |
| """Check if all the models are downloadable and tts models run correctly.""" | |
| print(" > Run synthesizer with all the models.") | |
| output_path = os.path.join(get_tests_output_path(), "output.wav") | |
| manager = ModelManager(output_prefix=get_tests_output_path(), progress_bar=False) | |
| model_names = [name for name in manager.list_models() if name not in MODELS_WITH_SEP_TESTS] | |
| print("Model names:", model_names) | |
| for model_name in model_names[offset::step]: | |
| print(f"\n > Run - {model_name}") | |
| model_path, _, _ = manager.download_model(model_name) | |
| if "tts_models" in model_name: | |
| local_download_dir = os.path.dirname(model_path) | |
| # download and run the model | |
| speaker_files = glob.glob(local_download_dir + "/speaker*") | |
| language_files = glob.glob(local_download_dir + "/language*") | |
| language_id = "" | |
| if len(speaker_files) > 0: | |
| # multi-speaker model | |
| if "speaker_ids" in speaker_files[0]: | |
| speaker_manager = SpeakerManager(speaker_id_file_path=speaker_files[0]) | |
| elif "speakers" in speaker_files[0]: | |
| speaker_manager = SpeakerManager(d_vectors_file_path=speaker_files[0]) | |
| # multi-lingual model - Assuming multi-lingual models are also multi-speaker | |
| if len(language_files) > 0 and "language_ids" in language_files[0]: | |
| language_manager = LanguageManager(language_ids_file_path=language_files[0]) | |
| language_id = language_manager.language_names[0] | |
| speaker_id = list(speaker_manager.name_to_id.keys())[0] | |
| run_cli( | |
| f"tts --model_name {model_name} " | |
| f'--text "This is an example." --out_path "{output_path}" --speaker_idx "{speaker_id}" --language_idx "{language_id}" --progress_bar False' | |
| ) | |
| else: | |
| # single-speaker model | |
| run_cli( | |
| f"tts --model_name {model_name} " | |
| f'--text "This is an example." --out_path "{output_path}" --progress_bar False' | |
| ) | |
| # remove downloaded models | |
| shutil.rmtree(local_download_dir) | |
| shutil.rmtree(get_user_data_dir("tts")) | |
| elif "voice_conversion_models" in model_name: | |
| speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav") | |
| reference_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0032.wav") | |
| run_cli( | |
| f"tts --model_name {model_name} " | |
| f'--out_path "{output_path}" --source_wav "{speaker_wav}" --target_wav "{reference_wav}" --progress_bar False' | |
| ) | |
| else: | |
| # only download the model | |
| manager.download_model(model_name) | |
| print(f" | > OK: {model_name}") | |
| def test_xtts(): | |
| """XTTS is too big to run on github actions. We need to test it locally""" | |
| output_path = os.path.join(get_tests_output_path(), "output.wav") | |
| speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav") | |
| use_gpu = torch.cuda.is_available() | |
| if use_gpu: | |
| run_cli( | |
| "yes | " | |
| f"tts --model_name tts_models/multilingual/multi-dataset/xtts_v1.1 " | |
| f'--text "This is an example." --out_path "{output_path}" --progress_bar False --use_cuda True ' | |
| f'--speaker_wav "{speaker_wav}" --language_idx "en"' | |
| ) | |
| else: | |
| run_cli( | |
| "yes | " | |
| f"tts --model_name tts_models/multilingual/multi-dataset/xtts_v1.1 " | |
| f'--text "This is an example." --out_path "{output_path}" --progress_bar False ' | |
| f'--speaker_wav "{speaker_wav}" --language_idx "en"' | |
| ) | |
| def test_xtts_streaming(): | |
| """Testing the new inference_stream method""" | |
| from TTS.tts.configs.xtts_config import XttsConfig | |
| from TTS.tts.models.xtts import Xtts | |
| speaker_wav = [os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav")] | |
| speaker_wav_2 = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0002.wav") | |
| speaker_wav.append(speaker_wav_2) | |
| model_path = os.path.join(get_user_data_dir("tts"), "tts_models--multilingual--multi-dataset--xtts_v1.1") | |
| config = XttsConfig() | |
| config.load_json(os.path.join(model_path, "config.json")) | |
| model = Xtts.init_from_config(config) | |
| model.load_checkpoint(config, checkpoint_dir=model_path) | |
| model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) | |
| print("Computing speaker latents...") | |
| gpt_cond_latent, _, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav) | |
| print("Inference...") | |
| chunks = model.inference_stream( | |
| "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", | |
| "en", | |
| gpt_cond_latent, | |
| speaker_embedding, | |
| ) | |
| wav_chuncks = [] | |
| for i, chunk in enumerate(chunks): | |
| if i == 0: | |
| assert chunk.shape[-1] > 5000 | |
| wav_chuncks.append(chunk) | |
| assert len(wav_chuncks) > 1 | |
| def test_xtts_v2(): | |
| """XTTS is too big to run on github actions. We need to test it locally""" | |
| output_path = os.path.join(get_tests_output_path(), "output.wav") | |
| speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav") | |
| speaker_wav_2 = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0002.wav") | |
| use_gpu = torch.cuda.is_available() | |
| if use_gpu: | |
| run_cli( | |
| "yes | " | |
| f"tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 " | |
| f'--text "This is an example." --out_path "{output_path}" --progress_bar False --use_cuda True ' | |
| f'--speaker_wav "{speaker_wav}" "{speaker_wav_2}" "--language_idx "en"' | |
| ) | |
| else: | |
| run_cli( | |
| "yes | " | |
| f"tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 " | |
| f'--text "This is an example." --out_path "{output_path}" --progress_bar False ' | |
| f'--speaker_wav "{speaker_wav}" "{speaker_wav_2}" --language_idx "en"' | |
| ) | |
| def test_xtts_v2_streaming(): | |
| """Testing the new inference_stream method""" | |
| from TTS.tts.configs.xtts_config import XttsConfig | |
| from TTS.tts.models.xtts import Xtts | |
| speaker_wav = [os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav")] | |
| model_path = os.path.join(get_user_data_dir("tts"), "tts_models--multilingual--multi-dataset--xtts_v2") | |
| config = XttsConfig() | |
| config.load_json(os.path.join(model_path, "config.json")) | |
| model = Xtts.init_from_config(config) | |
| model.load_checkpoint(config, checkpoint_dir=model_path) | |
| model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) | |
| print("Computing speaker latents...") | |
| gpt_cond_latent, _, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav) | |
| print("Inference...") | |
| chunks = model.inference_stream( | |
| "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", | |
| "en", | |
| gpt_cond_latent, | |
| speaker_embedding, | |
| ) | |
| wav_chuncks = [] | |
| for i, chunk in enumerate(chunks): | |
| if i == 0: | |
| assert chunk.shape[-1] > 5000 | |
| wav_chuncks.append(chunk) | |
| assert len(wav_chuncks) > 1 | |
| def test_tortoise(): | |
| output_path = os.path.join(get_tests_output_path(), "output.wav") | |
| use_gpu = torch.cuda.is_available() | |
| if use_gpu: | |
| run_cli( | |
| f" tts --model_name tts_models/en/multi-dataset/tortoise-v2 " | |
| f'--text "This is an example." --out_path "{output_path}" --progress_bar False --use_cuda True' | |
| ) | |
| else: | |
| run_cli( | |
| f" tts --model_name tts_models/en/multi-dataset/tortoise-v2 " | |
| f'--text "This is an example." --out_path "{output_path}" --progress_bar False' | |
| ) | |
| def test_bark(): | |
| """Bark is too big to run on github actions. We need to test it locally""" | |
| output_path = os.path.join(get_tests_output_path(), "output.wav") | |
| use_gpu = torch.cuda.is_available() | |
| if use_gpu: | |
| run_cli( | |
| f" tts --model_name tts_models/multilingual/multi-dataset/bark " | |
| f'--text "This is an example." --out_path "{output_path}" --progress_bar False --use_cuda True' | |
| ) | |
| else: | |
| run_cli( | |
| f" tts --model_name tts_models/multilingual/multi-dataset/bark " | |
| f'--text "This is an example." --out_path "{output_path}" --progress_bar False' | |
| ) | |
| def test_voice_conversion(): | |
| print(" > Run voice conversion inference using YourTTS model.") | |
| model_name = "tts_models/multilingual/multi-dataset/your_tts" | |
| language_id = "en" | |
| speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav") | |
| reference_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0032.wav") | |
| output_path = os.path.join(get_tests_output_path(), "output.wav") | |
| run_cli( | |
| f"tts --model_name {model_name}" | |
| f" --out_path {output_path} --speaker_wav {speaker_wav} --reference_wav {reference_wav} --language_idx {language_id} --progress_bar False" | |
| ) | |
| """ | |
| These are used to split tests into different actions on Github. | |
| """ | |
| def test_models_offset_0_step_3(): | |
| run_models(offset=0, step=3) | |
| def test_models_offset_1_step_3(): | |
| run_models(offset=1, step=3) | |
| def test_models_offset_2_step_3(): | |
| run_models(offset=2, step=3) | |